Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	19,718
2	New Jersey	18,519
3	Massachusetts	15,034
4	Rhode Island	14,765
5	District of Columbia	13,303
6	Connecticut	12,367
7	Delaware	10,240
8	Illinois	10,165
9	Maryland	9,763
10	Louisiana	9,284
11	Nebraska	8,143
12	Iowa	7,008
13	Michigan	6,499
14	Pennsylvania	6,282
15	South Dakota	6,184
16	Virginia	6,004
17	Mississippi	5,970
18	Indiana	5,726
19	Minnesota	5,006
20	Colorado	4,891
21	Georgia	4,708
22	New Mexico	4,321
23	Alabama	4,267
24	Tennessee	3,985
25	Utah	3,860
26	Arizona	3,813
27	North Dakota	3,783
28	New Hampshire	3,735
29	Kansas	3,681
30	Wisconsin	3,634
31	North Carolina	3,487
32	California	3,398
33	Washington	3,360
34	Ohio	3,322
35	Arkansas	3,227
36	Nevada	3,186
37	Florida	3,021
38	South Carolina	2,874
39	Texas	2,666
40	Kentucky	2,619
41	Missouri	2,469
42	Maine	1,925
43	Oklahoma	1,821
44	Idaho	1,788
45	Vermont	1,722
46	Wyoming	1,658
47	Puerto Rico	1,579
48	West Virginia	1,205
49	Oregon	1,167
50	Alaska	829
51	Montana	512
52	Hawaii	468

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	155
2	Arkansas	120
3	Mississippi	111
4	Utah	107
5	Virginia	106
6	Alabama	104
7	North Carolina	100
8	District of Columbia	89
9	Maryland	89
10	South Carolina	87
11	Iowa	82
12	Louisiana	76
13	Minnesota	73
14	South Dakota	73
15	Tennessee	69
16	Delaware	68
17	California	65
18	Illinois	65
19	Nebraska	64
20	Rhode Island	63
21	New Mexico	61
22	Connecticut	59
23	North Dakota	59
24	Washington	58
25	Nevada	55
26	Florida	53
27	Georgia	53
28	Indiana	52
29	Massachusetts	51
30	Kentucky	48
31	Texas	46
32	New York	44
33	Puerto Rico	44
34	New Jersey	43
35	Wisconsin	42
36	Pennsylvania	39
37	Michigan	38
38	Missouri	34
39	Colorado	32
40	New Hampshire	30
41	Ohio	30
42	Oregon	27
43	Kansas	26
44	Maine	26
45	Vermont	25
46	Alaska	19
47	Oklahoma	17
48	Idaho	16
49	Wyoming	15
50	West Virginia	7
51	Hawaii	2
52	Montana	2

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,554
2	New Jersey	1,375
3	Connecticut	1,145
4	Massachusetts	1,066
5	Rhode Island	754
6	District of Columbia	695
7	Louisiana	633
8	Michigan	592
9	Illinois	470
10	Pennsylvania	469
11	Maryland	459
12	Delaware	408
13	Indiana	344
14	Mississippi	281
15	Colorado	267
16	Minnesota	214
17	New Hampshire	210
18	Ohio	205
19	Georgia	204
20	Iowa	197
21	New Mexico	190
22	Virginia	173
23	Washington	153
24	Alabama	146
25	Arizona	144
26	Nevada	143
27	Missouri	135
28	Florida	126
29	California	118
30	Wisconsin	111
31	Kentucky	109
32	South Carolina	108
33	Nebraska	101
34	North Carolina	99
35	North Dakota	98
36	Vermont	88
37	Oklahoma	87
38	Kansas	81
39	Maine	73
40	South Dakota	73
41	Texas	64
42	Tennessee	61
43	Arkansas	51
44	Idaho	46
45	West Virginia	46
46	Puerto Rico	44
47	Oregon	38
48	Utah	38
49	Wyoming	29
50	Montana	16
51	Hawaii	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Rhode Island	8
2	New Jersey	6
3	District of Columbia	5
4	Massachusetts	5
5	Connecticut	4
6	Maryland	4
7	Delaware	3
8	Illinois	3
9	Mississippi	3
10	Alabama	2
11	Indiana	2
12	Iowa	2
13	Louisiana	2
14	Michigan	2
15	Minnesota	2
16	New Mexico	2
17	New York	2
18	Arizona	1
19	California	1
20	Colorado	1
21	Georgia	1
22	New Hampshire	1
23	North Carolina	1
24	Ohio	1
25	Pennsylvania	1
26	South Carolina	1
27	Alaska	0
28	Arkansas	0
29	Florida	0
30	Hawaii	0
31	Idaho	0
32	Kansas	0
33	Kentucky	0
34	Maine	0
35	Missouri	0
36	Montana	0
37	Nebraska	0
38	Nevada	0
39	North Dakota	0
40	Oklahoma	0
41	Oregon	0
42	Puerto Rico	0
43	South Dakota	0
44	Tennessee	0
45	Texas	0
46	Utah	0
47	Vermont	0
48	Virginia	0
49	Washington	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	123,804	1	99
Lake	Tennessee	103,193	2	99
Dakota	Nebraska	85,888	3	99
Lincoln	Arkansas	75,630	4	99
Nobles	Minnesota	73,836	5	99
Davidson	Tennessee	8,972	253	91
Richland	South Carolina	4,315	661	78
Pierce	Washington	2,591	1062	66
Orange	California	2,407	1137	63
York	South Carolina	1,730	1442	54

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,074	1	99
Terrell	Georgia	3,048	2	99
Early	Georgia	3,042	3	99
Randolph	Georgia	2,803	4	99
Northampton	Virginia	2,220	5	99
Richland	South Carolina	168	625	80
Davidson	Tennessee	105	838	73
Pierce	Washington	94	896	71
Orange	California	56	1194	61
York	South Carolina	36	1414	54

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.